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QS17 Preview: My AMH Numbers Sucked, But I Made This Baby Anyway

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Whitney Erin Boesel: “In my case, AMH may not be as important as fertility clinics and egg-banking startups want people to believe.”

Women are understudied in most disciplines. Reproductive health is the general exception, but even then research on male reproductive problems often outnumbers that concerning women. One result among many is that our understanding female fertility isn’t as complete as it could be. For example,  anti-mullerian hormone (AMH) levels have been used to estimate a woman’s ‘reserve pool’ of eggs. Though AMH may become the “Gold Standard” of fertility, it still isn’t clear what levels are ideal for each woman. If you’re looking to get pregnant, there is a certain range (high but not too high) that is considered favorable for conception. At QS17 Amsterdam, Whitney is going to share her experience tracking her AMH, attempting to increase it, and finally having a successful pregnancy despite low-ish levels of the hormone. She’ll also hold a breakout session on tracking hormones, menstruation and fertility.

Aside being a new mum, Whitney is a writer, scholar, and active member of the QS community who, among her other work, has written about the incorporation of technology into medicine, Biomedicalization 2.0, and the nature of QS movement, “What Is The Quantified Self Now?

QS17 Amsterdam is coming up in just over a month on June 17-18.
We hope to see you there!

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QS17 Preview: Dashboard of My Life

deSouzaDashboard

David de Souza: I’ve been recording 35 of the most important areas of my life – and using Google Spreadsheets to create a personal dashboard that tracks my progress.

Tracking 35 metrics might seem like a daunting task. Everything from quantum theory to tracking-based anxiety shows that the mere act of observing affects the observed. Automating personal data collection might help us stress less, collect more, and (hopefully) be more accurate.

David has managed to create a streamlined workflow allowing him to record everything from sleep, weight and food intake to productivity, yoga and meditation. At QS17, David is going to share this dashboard and the correlations he’s drawn between diverse aspects of his behavior. He says his dashboard has done ”wonders to keep me accountable, and more importantly, to help me notice when I have fallen off the horse, allowing me to keep on track with my goals.” For those of us (all of us) looking to optimize our workflows and understand our habits, this is definitely a talk to see.

Join us at QS Amsterdam June 17-18, and if you haven’t already, check out our latest program. See you there!

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QS17 Preview: My Life as a Comic Strip

andreascartoon1Andreas Schreiber: I’ll share my automatically generated comics strips of personal tracking.

Could a personalized comic strip change the way you see your data? At QS17, Andreas Schreiber will share what may be the first Quantified Self comic strips. Well, maybe the first actually based on personal data. Andreas is excited to share this project because techniques like this could make self-tracking easier and more fun. Andreas is a founder of PyData Cologne and the Cologne QS Meetup, and an advocate of open source code to help ensure the reproducibility of scientific research. He has previously given a Show&Tell talk on recovering from a stroke and has since founded a company which creates apps to help others do the same.

QS conferences are an amazing place to share ideas shaping the future of wearable devices, precision medicine, and personal understanding. Join us at QS17, June 17-18, in Amsterdam.

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QS17 Preview: Quantifying your Constant Companion

Obsessive_PhoneJoost Plattel: I’ll share my phone usage data, and how changes to my apps have influenced my habits. 

In a little over a month, the global QS community will come together in Amsterdam for the 2017 Global Quantified Self Conference to share what they’ve been learning with personal data. If you’re like me, your phone might as well be attached to your body. As my near-constant companion, my smartphone can a be powerful source of information on everything from my exercise to my communication habits, but can also be a bit of a time sink for mindless procrastination.

Over the past couple of years, QS Amsterdam meetup co-organizer Joost Plattel has been analyzing when he picks up his phone, and for how long. More recently, he’s been looking at how app changes (ahem, deleting Facebook) influence these habits. This summer at QS17, Joost will share the insights gathered from this project. It’s a classic case of how small and often unconscious decisions add up, and that slight behavioral changes can be useful in the long run. Thankfully, Joost is taking the time to do the math. Joost is a data strategist and public speaker interested in creating open source analytics for quantified-selfers. Joost has previously given talks on analyzing his public transit data and what he learned about tracking teams as a part of his startup Qount.us.

QS conferences are organized to support the exchange of ideas, and we’re always inspired by what we learn. The next one is coming up June 17-18 in Amsterdam. We’ll see you there. (Thanks to xkcd for the injection of absurdity).

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QS17 Preview: Data-Based Sculpture

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Stephen Cartwright: I create landscape sculptures based on data recorded over the course of several years. This summer in Amsterdam, I’ll show these images and talk about how I make them.

Sometimes the hardest part of self-tracking isn’t deciding what to track, or even being diligent about tracking. The biggest challenge can be creating captivating visualizations that deliver a message.

Stephen Cartwright is taking data visualization to a new level. He’s the associate director at the School of Art and Design at the University of Illinois at Urbana-Champaign and himself an artist who uses self-tracking to create programmable sculpture, 3D acrylic topographies, and even acoustic representations of jet-lag. At QS17, he’ll be sharing some of these fantastic sculptural representations of his data with us.

Stephen is a familiar face at QS conferences. You may have seen one of his moving sculptures at QS15 or his talk on 17 years of location tracking. His art draws inspiration from data he collects about his family’s travels, his exercise, and his interaction with the environment. The results – including moving, color changing sculptures depicting temperatures through the seasons, and animations of his family’s migration through the United States – render an incredible image of change over time.

For those of us looking for clear ways to deliver personal data or research in outside-the-bar-graph ways, Stephen is an inspiration. If you’re an artist, scientist, engineer, doctor, designer, or just interested in the future of self-tracking and how novel data visualization can be used to understand ourselves and the world around us, check out our amazing program and join us this summer in Amsterdam!

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QS17 Preview: Can a Picture Be Worth a Thousand Numbers?

3D Body ScanA year of tracking my body shape with 3D scans
Body shape has been shown to be a better predictor of lifestyle-induced disease than BMI. Three-dimensional body scanners enable the 3D visualization of the body and the extraction of anthropometric landmarks and measurements. I wanted to see how body scanning compared to other ways of measuring physical variations (and how it felt to “get scanned”). 

On June 17-18, the global Quantified Self community will come together in Amsterdam to share what they’ve been learning with personal data.

We weigh ourselves, assess our body composition, and measure our waist and limbs. But would seeing how the shape of our entire body changed over time be a stronger motivator than numerical data? Psychological motivations aside, 3D body scanning may replace BMI as a go-to health measurement. It can help estimate body fat distribution, which correlates with cardiovascular disease and cancer risk. On a fun note, 3D body scans could help us find better fitting clothes.

At the 2017 Quantified Self Global Conference, Laila Zemrani will share how a year of her monthly 3D body scans helped her estimate body fat percentage and how her shape has changed with fitness and postural therapy. You may have seen Laila’s great Show & Tell talk at a recent QS Boston meetup on how strength vs. endurance training affected her body fat percentage. She also recently launched Fitnescity, a company that integrates health data – including 3D body scans – into personal wellness coaching, and contributes data to kinesiology research.

QS17 is the perfect event for seeing the latest self-experiments, discussing the most interesting topics in personal data, and meeting leaders in the Quantified Self community. We can’t wait to see you there.

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QS Amsterdam 2017 Preview: A Year of Psilocybin Micro Dosing

Janet_NEW2Sub-Perceptual Psilocybin Dosing
I’ll share data from my 12+ month experiment with sub-perceptual doses of psilocybin for the purposes of increasing social skills through decreased anxiety and elevated mood, empathy, and verbal fluidity.

As the conference draws closer, we want to share a little about some of the exciting speakers you’ll see there. The conference program is built from the ground up with attendees submitting their projects and ideas when they register. It’s always fun to read about someone’s new self-tracking project or experiment, especially when it involves something we haven’t seen before. Today we’re going to begin our conference previews with an especially novel talk.

Janet Lai Chang is a businesswoman, endurance athlete and “Psychedelics Biohacker”. At QS Amsterdam, Janet will share the results of over a year’s worth of psilocybin microdosing on her mood, empathy and social interactions.

Because most academic funding comes from government entities, psychedelics are often taboo. Investigations into the mood-elevating and potentially therapeutic effects of compounds like psilocybin are slowly being published, but because research tends to average across individuals, subtle personal effects can wash out. Quantification of individual experiences are especially fascinating and valuable. Janet hopes that her experiments can promote both research and personal exploration to help “bridge the gap between the mutually exclusive worlds of work and life”.

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Hot Stuff: Body Temperature Tracking and Ovulatory Cycles

For the past eight months I’ve been tracking my temperature every minute using small, wireless sensors.

I work in a lab that recently showed minute-by-minute body temperature can tell you fascinating things about female physiology, at least in mice. Using temperature, we can tell what day a mouse will ovulate, whether or not it will become pregnant within hours of pairing with a male, and in the same time, whether or not its pregnancy will be successful. Just as interesting, the temperature reveals that some mice have stable ovulatory cycles and some don’t. We wanted to see if any of this holds up in humans (read: lab mates, a sporting family member and myself). I’ll show you what we did, what we found, and how to get started if you’d like to start tracking your temperature too.

Why Temperature?
Think of metabolism as a continuous symphony and body temperature as the din that carries through the concert hall walls.

Many of the metabolic reactions taking place throughout our bodies generate small amounts of heat and are actually coordinated in a similar way to musical chords. For example, during the luteal phase of the menstrual cycle progesterone levels will pulse in concert with estradiol, often following a luteinizing hormone pulse occurring 15 minutes prior. These fluctuations, as well as other things that affect metabolism (ovulating, eating a meal, etc.), translate into small temperature ripples which register on the surface of the body.

Temperature has long been used as a predictor of ovulation. But most temperature based techniques rely on a single measurement per day. Limiting data collection to one time point per day is the equivalent of listening to the symphony only at what we hope is the crescendo of each piece: with training, we might identify the chord, but we’ll still miss most of the show.

What are we doing and what have we seen already?
To see if we could use high temporal resolution temperature to recapitulate any of our previous findings, we began monitoring distal (wrist), axial (arm-pit) and core (ahem, core) temperatures every minute, using small devices called iButtons. We’ve seen some interesting things so far. I’ve shown the temperature data as a heat map, because it allows you to see many measurements while giving a clear picture of the overall pattern of rising and falling average temperatures over the course of 28 days.

HeatMap_LabMate

Temperature Can Predict the Start of Menstruation.
In the graph above, which uses my lab mate’s data, you’ll see that the range of temperatures she passes through in a day shifts a little higher every day leading up to the start of spotting/ menstruation. This timing is clear in her data, but it isn’t identical for everyone, though. My cycles are irregular and the chart below shows that menstruation starts when my average temperature reaches its highest level of the month. Note that this can be more than once per 28 days, as in the month graphed below.

heatmap_Azure

In my mom’s case, the heat map below clearly shows the shift between follicular (cooler) and luteal (warmer) phases. I’ve outlined the profiles of the Progesterone (P) and Estrogen (E) that my mom takes each day as part of hormone replacement therapy. In the valley where both hormones are low, she transitions from follicular to luteal phase. This corresponds to a temperature increase, and a few days later she gets her period.

HeatMap_Mom

These findings keep us coming back for more: more subjects and more longitudinal data for each of us. Perhaps the differences we have observed between us support that there are different ‘types’ of cyclers in the population, just as there are different body types. And maybe the temperature features we have in common will apply to other women.

So how do we gather the data (and how might you)?

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iButtons are about the size of the button on your jeans, and one side has a sensor which is worn pressed to the skin. A sweat band is enough to secure one button to your wrist, and the axial button can be tucked into a bra strap or secured with a non-irritating skin tape (here is my favorite so far). Body temperature shouldn’t ever fluctuate more than a couple of degrees C, so devices with high precision are key. This model is accurate up to .0625 C. Both the resolution and the sampling rate can be user-specified, meaning you can take very precise measurements very frequently. I find that anywhere between one and three-minute resolution works well to capture changes throughout the day.

iButtons don’t ever need to charge, but the data needs to be read once the memory fills up. Depending on the sampling rate, that’s every 3-7 days. At the end of a recording period, the ibutton is touched to its reader, and a simple interface allows the user to view the data and export it as a csv. iButton will plot the data, but it won’t do any further analysis. We’ve taken these csv outputs into Matlab and Python for our analyses, and because they are widely used formats, anyone could make graphs and start to play with their data. I’m not associated with the company, but I’m excited to share what we’re finding and want others to know how to jump in. An ibutton and a reader together cost about $100.

Interested?
Temperature tracking is a scavenger hunt: we don’t know precisely what we’re looking for, but clues keep turning up that lead us in interesting and verifiable directions. Multiple hormonal systems in our bodies (the stress axis, the digestive system, the thyroid axis) affect body temperature, and the reproductive system is just one of those. This raises the question: could we see predictable changes in temperature associated with a long run, a large meal, or a bad night of sleep? Probably. Mapping the personal, research, and clinical applications of high temporal resolution body temperature tracking will take time and user participation. Luckily, it gives interesting and useful personal feedback along the way.

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